Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing

Li, Chunlin; Song, Mingyang; Luo, Yingwei

doi:10.1016/j.eswa.2023.121023

Cited by 15 publications

(1 citation statement)

References 26 publications

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“…While early federated learning research focused on distributed optimization problems, in recent years, with the rise of deep learning, federated learning has been gradually applied to a wider range of tasks, including the Internet of Things, cloud servers, and medical diagnosis [16]. For example, a federated learning framework for UAV edge computing based on the Stackelberg game is proposed in the literature [17] to cope with traffic surge events in healthcare infrastructure to improve the benefits of social good, in which the incentive mechanism is introduced into the federated learning framework to effectively improve the performance of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

Shi,

Zhou,

et al. 2023

Energies

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With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance.

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Section: Literature Reviewmentioning

confidence: 99%

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

Shi,

Zhou,

et al. 2023

Energies

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A pricing strategy for federated learning in UAV-enabled MEC

Song,

Li,

Luo

2023

J Supercomput

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RCFS: rate and cost fair CPU scheduling strategy in edge nodes

Zhao,

Rao,

et al. 2024

J Supercomput

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The cloud-edge collaborative edge computing paradigm is driving the comprehensive development of the Internet of Things. With the widespread deployment of 5G mobile networks, IoT end devices tend to offload data to more powerful edge nodes for computing, thereby improving the speed. This has led to an explosive growth in the amount of data processed by edge nodes. General CPU scheduling strategies are unsuitable for edge nodes that frequently occur in data-intensive applications. In this study, we focus on application feature awareness and design a strategy that considers data arrival rate and cost-fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. Compared to the Linux default scheduling strategy, CFS, experiments on edge nodes show that the RCFS strategy can effectively improve CPU utilization, decrease the running time of data-intensive applications, and improve the system's data throughput. In the best-case scenario, the RCFS strategy enhanced CPU utilization by up to 98.5%, decreased running time by up to 45.5%, and increased data throughput by up to 58.7%.

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Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing

Cited by 15 publications

References 26 publications

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

A pricing strategy for federated learning in UAV-enabled MEC

RCFS: rate and cost fair CPU scheduling strategy in edge nodes

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